{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/pyaneti-a-fast-and-powerful-software-suite","title":"Pyaneti: a fast and powerful software suite for multi-planet radial velocity and transit fitting","arxiv_id":"1809.04609","date":"2018-09-12","proceeding":null,"authors":["O. Barragán","D. Gandolfi","G. Antoniciello"],"abstract":"Transiting exoplanet parameter estimation from time-series photometry and Doppler spectroscopy is fundamental to study planets' internal structures and compositions. Here we present the code pyaneti, a powerful and user-friendly software suite to perform multi-planet radial velocity and transit data fitting. The code uses a Bayesian approach combined with an MCMC sampling to estimate the parameters of planetary systems. We combine the numerical efficiency of FORTRAN, the versatility of PYTHON, and the parallelization of OpenMP to make pyaneti a fast and easy to use code. The package is freely available at https://github.com/oscaribv/pyaneti.","url_abs":"http://arxiv.org/abs/1809.04609v1","url_pdf":"http://arxiv.org/pdf/1809.04609v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"pyaneti-a-fast-and-powerful-software-suite","repo_url":"https://github.com/oscaribv/pyaneti","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}